Thai personal named entity extraction without using word segmentation or POS tagging

Named entity (NE) extraction for Thai language is a difficult and time consuming task because sentences in Thai language are composed of a series of words formed by a stream of characters. Moreover, there are no delimiters (blank space) to show word boundaries. Currently, most named entity extraction methods for Thai language are associated with word segmentation and part of speech (POS) tagging processes. The accuracy of named entity extraction is mostly affected the efficiency of those processes. At present, it is still lack of suitable methods for identifying the boundary of word for Thai sentence. Therefore this paper proposes the method to extract Thai personal named entity without using word segmentation or POS tagging. The proposed method is composed of 3 steps. Firstly, pre-processing, this process is used to remove non alphabet such as parenthesizes and numerical. Then, personal named entity is extracted by using contextual environment, front and rear, of personal name. Finally, post-processing, a simple rule base is employed to identify personal names. The training corpus of 900 political news articles and the test corpus of 100 political news, 100 financial news and 100 sport news articles were used in the experiments. The results showed that the F-measures in political and financial domain are 91.442% and 91.720% respectively which are nearly the same. However, the proposed scheme used neither word segmentation nor POS tagging process that can significantly reduce the effort and speed up the process in building the training corpus.